{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization using matplotlib and seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Visualization strategy: ** \n",
    "    \n",
    "    - Single variable\n",
    "        - numeric continuous variable\n",
    "            - histogram: distribution of values\n",
    "            - boxplot: outlier analysis\n",
    "        - Categorical (string or discrete numeric)\n",
    "            - frequency plot\n",
    "    - Association plot \n",
    "        - continuous vs continuous: scatter plot\n",
    "        - continuous vs categorical: vertical bar and boxplot (regression problems)\n",
    "        - categorical vs continuous: horizontal bar (classification problems)\n",
    "        - categorical vs categorical: heapmap\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from matplotlib.mlab import normpdf\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "plt.rcParams['figure.figsize'] = 10, 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>year</th>\n",
       "      <th>origin</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3504.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet chevelle malibu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>3693.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>buick skylark 320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3436.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>plymouth satellite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3433.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>amc rebel sst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
       "      <td>302.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>3449.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>ford torino</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>429.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>4341.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>ford galaxie 500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>14.0</td>\n",
       "      <td>8</td>\n",
       "      <td>454.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>4354.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet impala</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14.0</td>\n",
       "      <td>8</td>\n",
       "      <td>440.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>4312.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>plymouth fury iii</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>14.0</td>\n",
       "      <td>8</td>\n",
       "      <td>455.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>4425.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>pontiac catalina</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>390.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>3850.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>amc ambassador dpl</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mpg  cylinders  displacement horsepower  weight  acceleration  year  \\\n",
       "0  18.0          8         307.0      130.0  3504.0          12.0    70   \n",
       "1  15.0          8         350.0      165.0  3693.0          11.5    70   \n",
       "2  18.0          8         318.0      150.0  3436.0          11.0    70   \n",
       "3  16.0          8         304.0      150.0  3433.0          12.0    70   \n",
       "4  17.0          8         302.0      140.0  3449.0          10.5    70   \n",
       "5  15.0          8         429.0      198.0  4341.0          10.0    70   \n",
       "6  14.0          8         454.0      220.0  4354.0           9.0    70   \n",
       "7  14.0          8         440.0      215.0  4312.0           8.5    70   \n",
       "8  14.0          8         455.0      225.0  4425.0          10.0    70   \n",
       "9  15.0          8         390.0      190.0  3850.0           8.5    70   \n",
       "\n",
       "   origin                       name  \n",
       "0       1  chevrolet chevelle malibu  \n",
       "1       1          buick skylark 320  \n",
       "2       1         plymouth satellite  \n",
       "3       1              amc rebel sst  \n",
       "4       1                ford torino  \n",
       "5       1           ford galaxie 500  \n",
       "6       1           chevrolet impala  \n",
       "7       1          plymouth fury iii  \n",
       "8       1           pontiac catalina  \n",
       "9       1         amc ambassador dpl  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"http://www-bcf.usc.edu/~gareth/ISL/Auto.data\", sep=r\"\\s+\")\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 397 entries, 0 to 396\n",
      "Data columns (total 9 columns):\n",
      "mpg             397 non-null float64\n",
      "cylinders       397 non-null int64\n",
      "displacement    397 non-null float64\n",
      "horsepower      397 non-null object\n",
      "weight          397 non-null float64\n",
      "acceleration    397 non-null float64\n",
      "year            397 non-null int64\n",
      "origin          397 non-null int64\n",
      "name            397 non-null object\n",
      "dtypes: float64(4), int64(3), object(2)\n",
      "memory usage: 28.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"year\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>year</th>\n",
       "      <th>origin</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>38.0</td>\n",
       "      <td>4</td>\n",
       "      <td>105.0</td>\n",
       "      <td>63.00</td>\n",
       "      <td>2125.0</td>\n",
       "      <td>14.7</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>plymouth horizon miser</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>14.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>4096.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>plymouth fury iii</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>31.9</td>\n",
       "      <td>4</td>\n",
       "      <td>89.0</td>\n",
       "      <td>71.00</td>\n",
       "      <td>1925.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>79</td>\n",
       "      <td>2</td>\n",
       "      <td>vw rabbit custom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>25.0</td>\n",
       "      <td>4</td>\n",
       "      <td>98.0</td>\n",
       "      <td>?</td>\n",
       "      <td>2046.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>ford pinto</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>19.0</td>\n",
       "      <td>6</td>\n",
       "      <td>225.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>3630.0</td>\n",
       "      <td>17.7</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>plymouth volare custom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>32.0</td>\n",
       "      <td>4</td>\n",
       "      <td>85.0</td>\n",
       "      <td>70.00</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>76</td>\n",
       "      <td>3</td>\n",
       "      <td>datsun b-210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>27.0</td>\n",
       "      <td>4</td>\n",
       "      <td>97.0</td>\n",
       "      <td>88.00</td>\n",
       "      <td>2130.0</td>\n",
       "      <td>14.5</td>\n",
       "      <td>70</td>\n",
       "      <td>3</td>\n",
       "      <td>datsun pl510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>25.0</td>\n",
       "      <td>4</td>\n",
       "      <td>140.0</td>\n",
       "      <td>75.00</td>\n",
       "      <td>2542.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet vega</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>29.8</td>\n",
       "      <td>4</td>\n",
       "      <td>89.0</td>\n",
       "      <td>62.00</td>\n",
       "      <td>1845.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>80</td>\n",
       "      <td>2</td>\n",
       "      <td>vokswagen rabbit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>12.0</td>\n",
       "      <td>8</td>\n",
       "      <td>400.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>4906.0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>73</td>\n",
       "      <td>1</td>\n",
       "      <td>ford country</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      mpg  cylinders  displacement horsepower  weight  acceleration  year  \\\n",
       "377  38.0          4         105.0      63.00  2125.0          14.7    82   \n",
       "41   14.0          8         318.0      150.0  4096.0          13.0    71   \n",
       "293  31.9          4          89.0      71.00  1925.0          14.0    79   \n",
       "32   25.0          4          98.0          ?  2046.0          19.0    71   \n",
       "227  19.0          6         225.0      100.0  3630.0          17.7    77   \n",
       "204  32.0          4          85.0      70.00  1990.0          17.0    76   \n",
       "18   27.0          4          97.0      88.00  2130.0          14.5    70   \n",
       "132  25.0          4         140.0      75.00  2542.0          17.0    74   \n",
       "332  29.8          4          89.0      62.00  1845.0          15.3    80   \n",
       "104  12.0          8         400.0      167.0  4906.0          12.5    73   \n",
       "\n",
       "     origin                    name  \n",
       "377       1  plymouth horizon miser  \n",
       "41        1       plymouth fury iii  \n",
       "293       2        vw rabbit custom  \n",
       "32        1              ford pinto  \n",
       "227       1  plymouth volare custom  \n",
       "204       3            datsun b-210  \n",
       "18        3            datsun pl510  \n",
       "132       1          chevrolet vega  \n",
       "332       2        vokswagen rabbit  \n",
       "104       1            ford country  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization for a single continuous variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Frequency')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DhjBJkqQGDGGSJEkNGMIkSZIaMIRJkiQ1YAiTJElqwBAmSZLUgJO1SlpwugeH\n/z1wJYPHBH2DwWNPTmbwIPXjgaMYPMtyb2Bf4H9V1ZlJHgF8EHge8B0G/7N6dlVdMNpfIWmhM4RJ\nWqh+lcFTCpYDXwf+M/Bc4CUMZvBfDfw6g5D2aODaJBcDvwUsBv4dg8B2E3D2iGuXNAa8HClpofpO\nVV3fPTD8RuCyGjwi5HoGIQvgwqr6eVV9n8Ez+5YxCGqfqqqNVfVv3XpJmnOGMEkL1b2T3m+ctLyR\nB68CTH1uWzF4Tp0k9c4QJmmcHZNkpyRPYPBA+q8zGEf2n5I8Isme3XpJmnOOCZM0zq4GLgb2A95d\nVXcm+TTwAuAG4F+Aq4AftytR0kKVwRAJSRovSU4CNlTV+6b5bNeq2tD1kF0NHNqND5OkOWNPmCRt\n6aIkuwGPZNBDZgCTNOfsCZMkSWrAgfmSJEkNGMIkSZIaMIRJkiQ1YAiTJElqwBAmSZLUgCFMkiSp\ngf8POHcnDLOCDpIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0a874080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df[\"mpg\"], bins = 30)\n",
    "plt.title(\"Histogram plot of mpg\")\n",
    "plt.xlabel(\"mpg\")\n",
    "plt.ylabel(\"Frequency\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'mpg')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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y95JclUSEAQA8DCu9MP/YBwNssifJs7r79iT3rf5YAAAb20qPhP2vqvp0kt+dXr8iyVVV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\nPAREZKkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a09f30780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(df[\"mpg\"])\n",
    "plt.title(\"Boxplot of mpg\\n \")\n",
    "plt.ylabel(\"mpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,33,'MPG')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mmIfoPet1FtWux+Dd+/HPPsfwZtN2xS5Pkspl9nZN+cMBp/OX/b7McROHc/bo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\nDARkAAAYCMgAADAQkAEAYPA/t3yILLLBfQgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0a9eae10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.figure(figsize = (10, 6))\n",
    "plt.subplot(2, 1, 1)\n",
    "n, bins, patches = plt.hist(df[\"mpg\"], bins = 50, normed = True)\n",
    "plt.title(\"Histogram plot of mpg\")\n",
    "plt.xlabel(\"MPG\")\n",
    "\n",
    "pdf = normpdf(bins, df[\"mpg\"].mean(), df[\"mpg\"].std())\n",
    "plt.plot(bins, pdf, color = \"red\")\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.boxplot(df[\"mpg\"], vert=False)\n",
    "plt.title(\"Boxplot of mpg\")\n",
    "plt.tight_layout()\n",
    "plt.xlabel(\"MPG\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.00912588,  0.0108562 ,  0.0127959 ,  0.01494355,  0.01729125,\n",
       "        0.01982389,  0.02251859,  0.02534448,  0.02826282,  0.03122751,\n",
       "        0.03418606,  0.03708093,  0.03985125,  0.0424349 ,  0.04477073,\n",
       "        0.046801  ,  0.04847366,  0.04974465,  0.05057976,  0.0509562 ,\n",
       "        0.0508636 ,  0.05030452,  0.0492943 ,  0.0478604 ,  0.0460411 ,\n",
       "        0.04388387,  0.04144327,  0.03877867,  0.03595189,  0.03302481,\n",
       "        0.03005722,  0.02710485,  0.02421782,  0.02143942,  0.01880533,\n",
       "        0.01634325,  0.01407298,  0.01200669,  0.01014963,  0.00850095,\n",
       "        0.00705463,  0.00580057,  0.0047256 ,  0.00381446,  0.0030507 ,\n",
       "        0.00241744,  0.00189802,  0.00147651,  0.00113805,  0.00086912,\n",
       "        0.00065763])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normpdf(bins, df[\"mpg\"].mean(), df[\"mpg\"].std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'mpg')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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O6aYvBA5Pkm75eVV1T1V9DdjYfZ8kSdJU6TOs7QfcMDS/qVs2tk1VbQVuB5ZM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\nSQ0zrEmSJDXMsCZJktSw/weXZlB7mRxSBAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a09f309b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# using pandas plot function\n",
    "plt.figure(figsize = (10, 6))\n",
    "df.mpg.plot.hist(bins = 50, normed = True)\n",
    "plt.title(\"Histogram plot of mpg\")\n",
    "plt.xlabel(\"mpg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization for single categorical variable - frequency plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "counts = df[\"year\"].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Frequency distribution by year')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JPpbkSQusc3lVfbd/fh1wPrDrBuaVJElaEoYeE1ZVFwJHAK8DHg/8XZILkjxzoXWTrAAe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\nqthN2tBkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0adbd0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 4))\n",
    "plt.bar(range(len(counts)), counts, align = \"center\")\n",
    "plt.xticks(range(len(counts)), counts.index)\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"Frequency distribution by year\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Bar plot using matplotlib visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a0a8369b0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9MGs/zNqPWck61ZyrPgBdkiRJfp2MJElSE8uUJElSA8uUJElSA8uUtA8lOXLaGf45SnL4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\nBv8f9Vx2eQEDV5wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0a9dceb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 4))\n",
    "df.year.value_counts().sort_index().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Association plot between two continuous variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Continuous vs continuous"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Mpg vs Weight, correlation: -0.83')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wzLprGWsTLJ07sYNNm3ey150hM0aG57FnZt8B+zXGnxWdH03BXW/pdDksfeYi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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0afabfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = np.corrcoef(df[\"weight\"], df[\"mpg\"])[0, 1]\n",
    "plt.scatter(df[\"weight\"], df[\"mpg\"])\n",
    "plt.xlabel(\"Weight\")\n",
    "plt.ylabel(\"Mpg\")\n",
    "plt.title(\"Mpg vs Weight, correlation: %.2f\" % corr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scatter plot using pandas dataframe plot function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Mpg vs Weight, correlation: -0.83')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ODCissgJxlSewu46bYhviqrtVi7Wy7Ea1rr+0E3fv+HVJEFZtGQ2WsKitEVIi\norAxCCsTVl2suJbasbuOG2sbrAVZrT9Xo/xLtVYDjfIgFQA+/2hfyT7Vvp9pX44parU2QkpEFLZM\n0g1Im+IoyKTmDNpyTZjUnAlUFyus8/i9TlMGaM7K+DXXLpnn2IYtvQew9I7tWHPPLiy9Yzu29h6o\nqi1256vlQKO9NYdFc6eNj26F/X7G9RlJI+sI6eDwGE6NFrBu8x4cHRpOumlERLFhTpiDOJ+ODIP1\nOkDpE3x2bQg7H8ntfHHmn0UtivezEafk3Ep6LJo7LcGWERFVjzlhVQqrXEJcZRfKr1MpTynsaUK3\n89Xiuo9Oong/01KaI061PEJKRBSWyKYjRWSSiPxERHaLyF4R+ai5/VwR2SUiz4jIt0SkJao2NLqj\nQ8PY3T9gO8Vj9yU4nC9gdCzveEy5vkOD2NTTj75DgxW/VK1Te2Fyu0dKr2qmYvmeE1G9iGw6UkQE\nwBRVHRKRZgA7AbwHwPsBfEdVvykiXwSwW1W/4Haueq+YHwUvSc/FpYIA4NRoAU0ZYKwA5LICyYjr\ntOGHv/skNj6+b/zntUvmofucM2OddtzSewDrNu1GVjLIawEbVi+q2WnORuV3KpbJ/ERUC1K1bJGI\nvAxGEPZXAB4E8HJVHRORJQA+oqp/5HZ8owRhYeahec336js0iDd/7ocYyU/8HLgd88bP7piw/8Pv\nW47pU1piy4Fb/ImHYakYgaYMsOuWN9bM1F4j5oJVg3XViKhWpCInTESyAJ4A0Ang8wB+BWBAVYvZ\nuPsB2P4zVkSuA3AdAMybNy/KZqZCmP/C95PvdWIkj1xTFiP5sfLTOB7T2z9ge93e/gGs7p4byxfi\n3oMvlQRggDGKt/fgS1h+/szIr18tjuj4V6vlToiInERaokJV86raBaADwOsA/K7dbg7H3q2q3ara\nPXNm+r9Ugzo6NIwdvzyCdZvCe1zfT9KzW50xu2OODg1jUnPWdv+uWJ9qcxrBTf/TvizPEAyT+Ymo\n3sRSJ0xVBwA8CuBiANNEpDgC1wHgYBxtSKNiXa2//NoTJZXYgdP/wg/CT9Kzdd9JzcbHIZcV22OK\n7b35O08imyldHHztknnonNUWqL1BLJw9Fc3Z0jY0ZwULZ0+NrQ1BFUd0rKp5vxtFI9dVI6L6FNl0\npIjMBDCqqgMiMhnAGwHcAeARAKsBfBPA1QC2RNWGNLOOhtip9l/4fspCWPed0pLFiZH8hGPslh/K\nNWVw8x+/Ess6Z8QagAHGF/Kn37YIN27ag2xGkC8oNqyujS9kjugEV0/lToiIoswJOxvAvWZeWAbA\nA6q6TUSeBvBNEfnfAH4G4F8cAn2vAAAgAElEQVQibENqOa35+LKWLAqqofwL30/9qUr72rW3JZvB\nq+ZNjz0AK6rVL2S7tSk5ouNdI9ZVI6L6FFkQpqp7ALzKZvuvYeSHNTS70ZBck+CLa149PqW2u3+g\nYnDR8+xR7HjmBSx/xQx0n9vu+fp+n8yrNHrjdL6onwAM+ws5ricWazWAJCKi8LBifkKcRkOWn3+W\n5yfn1tzzOHb2HQUAfG57Hy7pbMfXrrm44rWDPJnnNnrjdL5aewIw7vZyRIeIqLFx7ciElY+8eK2F\n1PPsUaz+0uMTzrfpLy52HRGrttaS1/Zuu2EZVt61s2ZqOgXpF9b5IiIiO6moE0aVlY+GeK2FtOOZ\nF2zPt+OZF1yDsGprLXltb2//QE3VdPLbL7U2ykdEROnjKQgTkf9ps/k4gCdV9XC4TWpsXp+cW/6K\nGfjc9r4Jxy9/xYxQzl9te7vmTqupJwD99Ivdk6LrNu/B0s4ZqQwwiYgonbzWCXs3gHsAXGX++jKM\nNSAfE5E/i6htDclrLaTuc9txSWfpiNclne0Vk/PDrrXkdL7OWW01VdPJT7/4rfPFBaeJiMiOp5ww\nEfkegGtU9ZD58ywAXwBwDYAdqnpBlI2s55wwJ17zjeJ6OjLo+Wotb8pLe/3kj3Hakoio8YS6gLeI\nPKmqF1p+FhhTkReIyM9UdUIpijA1YhBG6ba198CEJ0XLgysuOB2eWgvmiaixhZ2Y/0MR2Qbg2+bP\nqwHsEJEpAOxXc6a603doEL39A+iaOw2ds9rGvxidquxXYv1iBRDLl2xYX+bFOl97D74EQLFw9tQJ\n57ZL9s+KpPbhhLTiaCIR1SuvQdj1AP4ngGUABMC9ADarMYz2+ojaRiny4e8+iY2P7xv/eVlnO3qe\nOwYAODVaQC4rkIx4/oK0frGeHB2DiGBSUzbSL9mwv8x39r3geg9LO2dMSPY/MZLHUwePY1HAxc4b\nbUSID0EQUT3zlJhvBluPwViE+wcAdmotFBijUPQdGiwJwABgZ99R44vRnGobzitOjRawbvOeigno\n1i/WweExjBWA0bxicHjM8zn8Kr9mtdfxcg8AcOtlCyYce/u2pwNdt7iA+pp7dmHpHduxtfdAoLbX\nEi52TkT1zFMQJiLXAPgJgD+BMRX5uIi8K8qGUXr09nufcfbyBWn3xer3HH6F/WXu9R4umDMVrbls\n1dcNO4isFVzsnIjqmdcSFTcCeJWqvlNVrwbwGgDro2sWpUmXj6kzL1+Qdl+sfs/hV7Vf5uVlJrze\nQ8f0yRgrqO1rfjTqiFDYJVWIiNLEaxC2H8Cg5edBAP3hN4fSqHNWG9YumVey7ZLOdkxqzmBSs/ER\nymXF8xdk+RdrUwZozkqkX7LVfJnbTQN6vYf21hxuXbkALU0ZTMllA99fI48Ireqag8fWr8B91yzG\nY+tXMCmfiOqG1xIVGwFcCGCLuWkVjOnJXwKAqn4mqgYCLFGRFnE8HRn0XNVcs5qaYJXOV3wYICuC\n0XwBt12+EFddfE6gtnspi1HrGu3BAyKqT2GXqPiV+asYsW0xf98WrHlUizpntaFz1um3vHwdSb/K\nj7c+bRhVkFG8ptcnJSutKVl+D+WFaot5XEW3P/g03nTBywP1W7EsRr0GKSxFQUSNxmsQ9n0AtwCY\nbzlGVfWiKBrVaNL6r3+7dpWPhvk5ttK14ipF4OdaQaYBi/d+/OSor0XBK/VZlJ+TpD+DLEVBRI3I\naxB2H4APAngKgHM2MvmW1n/927Wr5zcvlpSqWLtkHj52xYWejq10T5VGnMLk51rF3K/yaUCnNlnv\nfSRfQN5jAFepz6L8nKThMxjn+09ElBZeg7Ajqvq9SFvSgNL6r3+7dt24aQ+Gx0oDio0/3oe1F88v\nGRELek9xJp77vZbXaUC7e2/OCnJNQEs2WxLAleeSufVZlJ+TtHwGG/nBAyJqXF6DsNtE5B4YhVrH\nCxOp6nciaVWDSOu//u3a5aS3f6AkCAt6T35HnKoR5Fpe8t/2HzuJpoyUbJvUlMXnr3oVpk5uGQ/g\nykeerr+007XPovycpOUzGOf7X2+SnkomouC8BmF/DuCVAJpxejpSATAIq0Ja//VfqQaWVXkNsWru\nKc7Ec7/X8vJF99SB4xgazpdsGy0UsHD21PFj7Eae7nrkGRirgZUeV+yzKD8nafoM1vuDB1FIw1Qy\nEQXntU7YIlXtVtWrVfXPzV+smF+ltBaitGvXhtUXTagVtnbJvAnJ+dXeU3trDovmToulD7xey8ty\nQUeHhnH7g09P2H7rygUl57crutqSzeKG13c69lmQPi0vLuvWB2n6DMb5/te6Rl1FgaieeB0Je1xE\nFqjqxG8Zqkpa//Vv165VXXOw9uL5FZ+OTOs9BeE1Z8puWm9KSxYXzJ5acj6nkad3LJ6Hdyye59hn\nfvrU7+hIPb1fjSQtU8lEFJzXIGwZgKtF5FkYOWEClqgITbX1tqJi167yWmF+jq1FXr/o7IKrvOqE\nab1KuU/V5qUFTbSvl/erkaRpKpmIgvEahL0p0lY0uDASa48ODWPvweN46eQYzpjcVJKHFCe3ezHa\n+BIAxeypk/GL/34JLwyNYFnnjPHALkhfeDkmaB/bfdGN5PM4fnIER4eGJ0wZekksj3LkiaMjjYMP\nMxDVPk/LFiWtnpctCiOxdkvvAXzw27sxmj/9XjZnBZ9+26JYk3Td7mVL7wF84IFejDnk+69dMg+v\nOedM333hpf+q7WPrckEnR8cgIpjUlC05V7VLOIWl0jJLVH/4dCRR+nhdtohBWILC+MI8OjSM3/vk\n9gk1vAAg15TBj26K58vX7V4A4Pc++QMMj7l/1lqyghFLIFmpL7z0X1hBSXEU79qNPSV9Pak5g1sv\nW4DbH3w6NU+oNcIak0REaRb22pEUgTCmjvYfO4lsWW2qomxGYpuGcrsXAMhKBkDe4WiDiOD08qSV\n+8JL/4U1PdfemsPUyc1oyWZKgrBsRvDR7+3FSF5TU3CXifZERLWBQViCwkis7Zg+GfmC/QhTvjAx\nMTwqle4lr5XrjpWPylbqCy/9F2bysu258ormbAYj+dMBZhg5WNVOMTHRnogo/bzWCaMIhFGjqb01\nhw2rL0JztnQ0rDkr2LA6viRdt3sx2rgITS6ftrVL5uHtr51bsu3K7g5PVfbd+i/MOlh257rt8gXI\n+wweK/FSl8wvr3XDqHrsayLyijlhKcCnI2dg+pSWwLlbUT4d6eVcYeZgRZFYz6rq8WFfExGQgpww\nEZkLYCOAl8NY6uhuVf1HEfkIgGsBHDF3vUVVvx9VO2pBGFNH7a05LD//rJBaVF07nO7FaOPM8Z+t\n9cZ29w8Ezt3y0n9hTs+Vn6vaHCxrUBd2iYm0LNDdCNjXRORXlDlhYwA+oKo/FZE2AE+IyEPma59V\n1U9FeG3yyM8IUdDRpL5Dg9jZdwQzWidhye+02x4bJHcr6kfzy8/vdj23IK84SgkIFs4+o2S/8pGT\nW1cuCLUAp5egjiUOwsEabUTkV2RBmKo+D+B58/eDIvJzAByXTxE/UydBp1k+/N0nsfHxfeM/C4B/\nfHvXhGP9Fp6Metqn/PxXvqYDDzyx3/f1ymu4NWWAz1zZNV5brHzk5PZtT9uWvAj6JV4puOX0WXhY\nwZ6I/IolJ0xE5gPYAeACAO8H8E4ALwHogTFadszt+HrPCUuCn9yjoHlKfYcG8cbP7piwvSUL/Pjm\nN9oe6zW/K8qCpHbnL+flek413HJNgh/d9AbsP3YSa+7ZhcHhsfHX2nJNuO+axeNTk2GMTjnlrLGw\na/hYo42IgBTkhFka0gpgM4D3qupLIvIFALfDKAh1O4BPA3iXzXHXAbgOAObNmxd1MxuOn6mToNMs\nvf0DttsFzsd6yd2KetrH7vzlvFzPqYZbVjLjAZbTyEmYOWxOOWucPgsfa7QRkR+RlqgQkWYYAdj9\nqvodAFDVQ6qaV9UCgC8DeJ3dsap6t6p2q2r3zJkz7XahKviZOqm0r9Mj+V1zp9leW1HdFE3U0z52\n5y/n5XpONdzyejrQspa8yDVlcP2lnVW13Ul7aw6L5k6ruOg4p8+qZ9fXRER2IgvCxCh//i8Afq6q\nn7FsP9uy258AeCqqNpAzP/Wz3PZ1q2nVOasNa5eUjmIKgE+9rauqL6gwa395Pf/aJfN8X8+uhltT\nBtiwetH4sau65uCx9Stw7fLzACju3vHr0GqDVeK1H1n3iogoGpHlhInIMgA/BPAkMD7fcQuAPwXQ\nBWM68jcA/sJM4nfEnLDoVPN0pNecIi9PR0bd9jDOH/R6bk9HFl9PIjfLy6LjTNwnIvIv8ZwwVd0J\nY+CjXEPXBEsbP7lH5ft6zSnqnNVWUhMsLFEvzVN+/qDXq1TDLYncLLvgalHZ9DHrXhERRYvLFlFF\nTtNRHdMnYyRffU5RkOkuu2PKt1U6b7WvB21n+fawc7O83FcxuBocHsOp0QLWbd4zYf9icGiVETFH\n9eoXp1+JKC5cwJtcuU1H7ex7AXlL8NCcFd+5WUGmu+yOUaC0rld3Bx7oca7rVem6YUzDOZ3Dbruf\nGmlu7n/8OXx029NoyQrGCmrbbq8jb3bB4W9H8rh2Yw82rF5Ul9OSnH4lojhx7Uhy5JarBGDCa8X6\nV16DhyC5UHbH5JoEgEyox2VlPW+l64aRo+V0jm03LMPKu3Y69mk1OW73P/4cPvTd0udc7Nrt5/62\n9h7AjZv2TOjbeqwnxrppRBQWrzlhnI4kR3bTUcURE7vXWrJZ7D92MpTz+zkmKxnbelxO56103SDt\n8tLO5kwGveYamXbnrqa0wdGhYXz0e3snbM9mZLzdxWk2AJ6fLl3VNQdfXtuNlzVnbdtcT8J434mI\n/OB0JDmqlKtUbR5TkFwou2PyWgDUPQiznrfSdcPI0XI6R9fcaRO2j+TzOH5yBEeHhgOPuOw/dhLN\n2QxG8vnSa+YVHdMn206zPbZ+haeRt4Wzz0ABpSPm9VhPjHXTiChuHAkjR251pMKo1RXkHHbHbFi9\nCBtWX4SWrH0g1lKWq1bpulHeW+estpLtTRmgoMD19/+sqvpgHdMnI2+TWnDb5QsAwDYRH4Cnkbeo\n67KlRaPcJxGlB3PCqCK3+lhh1OoKcg67Y/oODeLN/7QTI5b8pZamDL7/N8tsS2RUum6U92bUDnsJ\n127sKcm3yjVl8OW13bb1xCoprluYFcFovoDbLl+Iqy4+B7v7BxzXqCwvSxHkXupNo9wnEUXHa04Y\ngzCqK7W0gLJdcAQAL2vOogD7JxsrsQsgmHBORBSvxIu1EhU5BQZRjDYEXUDZb3uqGR20Vqq3W6Py\nt6NGXleQwqh2BWWL02w3btqDbEaQN0tXRDGqSfWJnw2iaDAIo0h5qekV9miV38r2fmtDue3vt/5Y\nsZ5ZRgS/HSlNqg+zar4W/6sy/lM1fUCNg58NouhwOpIi47WmV5JTY36n6vzWTvNSf2zbDctw8PhJ\nMz9MbY+N8h45XUlO+NkgCoZ1wihxXmt6JVmLyW9tKL+107zUHzsxksfy88/ChtWLInkyL466aFZc\n9qd+sHYaUbQ4HUmR8VrTK8laTH5rQ1VTO63SsUHz2SqJoy5aUa1PXTH3qRRrpxFFiyNhFBm3ml5p\nqcXktzZUNbXTvFyrmqr5bq6/tBO5JomsLhrgfWHwtNrSewBL79iONffsqqpmWz1h7TSiaDEnjCIX\n59ORQSXxdGQc924dmRrJF3DD6zvxjsXzImlXWPXIksDcJ3dp+/NKlHYsUUGp4VQ2IYyE8+IXA4Dx\nsg8nRvKRf1m4tb/SvYV978dOjKC3fwBdc6eVFKW1jkydghFcfP7RPrxj8TzHc1UTLEU5dRVWEOB0\nnmLuU7GfgHCfTq11YXxmiWgiBmFUk6wjPKfG8lBVNGUzODVaQC4rkIx4zkeqtTwma3tPjIyhYBnM\nXrtkHj52xYUAvAUWYd57ceqqvFhutV/eYbXR7TzMfSKiJDAnjGpOee7RaF4xVsD4VNJwXj3nI9Va\nHlN5ewtl2QQbf7wPfYcGAVQOLKK491Vdc/DY+hW475rFeGz9iqqD2bDaWOk8zH0ioiRwJIxqjt0I\njx0v00m1Ng3l5d57+wfQOaut4shUVPce5tRVWG30cp6onk4lInLCIIxqjt0Ijx0v00nVTENFnadk\nt9/xkyMYyecd9wGALktel1tgUQtTcHZtHB7LY0pLturz2N0rc5+IKE6cjqSaUz511JwVNGWMp9kA\nIJcVz9NJQaehwipn4PU8xf2uv/9nKCjQlDGePCyre4u1S+aVJOcX79Gu7EUtTMFZ25jLGjebyQhW\n3rXTV5/Xwr0SUeNhiQqqWWE+HelnVCuscgZez2O//FMGX17bjYWzz3B8OtJPO9I+Bdd3aBBv/qed\nGKlyuatauFciqn0sUUF1r3zqKK48pjjzlJz2a8lmMHVy83i7gwRfRbUwBXdiJI9cNlMShAXp81q4\nVyJqHJyOpMjFtZag1+s47ef1+CktWQzn3fOLvJzLa55S0Nytavq90rFxrw9ZC/lrRER+cSSMIhVX\nDS6v13Haz+/xYk7jF/PQrPlFXs/lta5WkPpb1fR7pWOTqKsWVQ0yIqIkMSeMIhPXUjDV5FZNas5g\n2w3LsPKunYGOb8kKvv+3l4xPBwa5Zz9PR3rdL2i/VzrW7XUAkedbMaeLiGoBc8IocXHV4Komt6o5\nk0Fv/0Dg43NNxoMA1dyz1zwlr/tV0++VjnV6/f5d+/DPj/ZFPjoW5pJPcS1xRUTkhEEYRSauPJ5q\nc6u65k4LLTcrDblL1bSh0rF2r4/k8/j8I30YHju9RuW6zXuwtHNGKCN7YSpOpQJwXeLq6NAw9h58\nCYBi4eypDNKIKBJMzKfIxFWbyet1nPbrnNVW1fHlT2gmXY+qmjZUOtbu9Rte/wq0ZEv/KimOnjkJ\nq86aHyULmrsscbWl9wAWf+JhrP3KT7D2K/+Ji//+B7G0j4gaD3PCKHJxjXhUm1sVZm5WGu65mjZU\nOra8RpufHLS4cgXL7e4fwJp7dmFweGzCa225Jtx3zWJ0TJ+M3/vkDzA8Vvr3Yq4pgx/dFG37iKh+\nMCeMUiOu2kzV5laFmZsVxz1XekoxSBuswdUiy/JH5aznPjo0jOsv7cRdjzyDlmy24pOLYeUK+g0y\n3Za7Kk657j92ElnJAChdGiqbkapzGflQARGViywIE5G5ADYCeDmAAoC7VfUfReRMAN8CMB/AbwBc\nqarHomoHpV+YX05xfNEl/WVazFdat2lPqHlYQUpPWI8BBNctPw/vWDxv/ElKpzUrRyrUWavU5iBt\ntZa5ACbmhBXPndeJgVq+oFXl9fltb9KfMSKKR5QjYWMAPqCqPxWRNgBPiMhDAN4J4Aeq+kkRuQnA\nTQDWR9gOSrEwa07FUb8qiRpZdtfPQDA8VhosVBpNcmt7Sb6Ux6DO7pjPP9qHdyye53qtnX0vIG8Z\nkWrOiuPImd15lnbO8N3WolVdc7Dg7DPQ2z+A+e0vQ3NTtiTQaW/NYcPqRXj/A70odm9zVrBhdfC8\nPr99m/RnjIjiE1kQpqrPA3je/P2giPwcwBwAVwC41NztXgCPgkFYQwryxR/HuZK8htfr26k0muTW\ndrspwqwIHvnFYbz+lWfZ3p/TtOLeg8cdrwUA6zfvgTV+zAjGX/PS5rv/7DWBpzPtApzyaddVXXOw\ntHNGaE9H+pl+TfozRkTxiuXpSBGZD+BVAHYBmGUGaMVA7SyHY64TkR4R6Tly5EgczaSYFb+crCo9\nVRfHuZK8ht/rA8DLWrIVn4Cs1Ha7fKkTI3l85Ht7HZ9edCpnAYjjteza0ZLN2vahU5sBCbyMUzHA\nGRwem/BUpFV7aw7Lz5+J5efbB6B++CkZkvRnjIjiFXkQJiKtADYDeK+qvuT1OFW9W1W7VbV75syZ\n0TWQEhNmTa046nMlXQPM7vq5JsEX17waj61f4TplVant1tITU1qy4/sMDecdgxWnchYLZ5/heC0/\nfei078LZZwQqwZFUgOOnZEjSnzEiilekT0eKSDOMAOx+Vf2OufmQiJytqs+LyNkADkfZBkqvMNcD\njGNtwaTXL3S6/vLzbQeTPR1rbXtxGu6RXxzGR763F0PDp58QdJo+Kx5TnkTudi0/fej01KXTdd0k\nGeB4bW81nzEm8xPVnsjqhImIwMj5elFV32vZvgHAUUti/pmqus7tXKwTVt/4dGR81/da4yyMOl7V\n1DCz5m6N5Au44fWd409dVmNr74EJAU4ak979vsdM5idKF691wqIMwpYB+CGAJ4HxjNRbYOSFPQBg\nHoB9AN6mqi+6nYtBGFG8kgxWoi7mWl5otprCvV6uEfXSTUkVvyUiZ4kXa1XVnQDE4eU3RHVdqg+1\nushy0qNkbu0o3+bW1iDTfWHx+jRhsV7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9Ob3ud3uY91bpem6lQertz2Ia/77hSBgRJaJe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\necAk9TkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0ae7d7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x= \"weight\", y = \"mpg\")\n",
    "plt.title(\"Mpg vs Weight, correlation: %.2f\" % corr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Continuous vs Categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>median</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>16.0</td>\n",
       "      <td>5.339231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>19.0</td>\n",
       "      <td>6.591942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>18.5</td>\n",
       "      <td>5.435529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>16.0</td>\n",
       "      <td>4.700245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>24.0</td>\n",
       "      <td>6.420010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      median       std\n",
       "year                  \n",
       "70      16.0  5.339231\n",
       "71      19.0  6.591942\n",
       "72      18.5  5.435529\n",
       "73      16.0  4.700245\n",
       "74      24.0  6.420010"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg_by_year = df.groupby(\"year\")[\"mpg\"].agg([np.median, np.std])\n",
    "mpg_by_year.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'MPG')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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w6+Y9Fss5YVVVD1XVP1bVG4GnMThf4SAGJxWOErP2w6z9MGs/zNqP+ZL1cd0h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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0ab17400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpg_by_year[\"median\"].plot.bar(yerr = mpg_by_year[\"std\"], ecolor = \"red\")\n",
    "plt.title(\"MPG by year\")\n",
    "plt.xlabel(\"year\")\n",
    "plt.ylabel(\"MPG\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Show the boxplot of MPG by year  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a0b494e10>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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P2c/GZ10OfCQznwDIzIeLJvtJP/G+RkQAv0nrj0U/GZ81gYMtSrOBZrFUExuf\n9VnAV6rlNwP/pViqJ5sBHBMRM4BjgQeBs4HPVK/309+sQ7M2M/M7mXl/2VgTmijrF7MCfIse/s0a\nxiLsdfz7h9eCzHwQoLo/uViqiY3P2u8Ol/WNwD/0OMtknpQ1Ii6LiB8CFwDvL5ZqYj/OGhHnAT/K\nzO+WjTShQ///31qd5r0m+uA0/yHGZ30m8KLqFM//i4jnFsw1kYl+r14EbM7MewvkOZLxWd8B/En1\ne7USuKRYqomNz3o3cF71+LW0WpuLyswf0XrfNtEqvnYAdwDbx33BfYBWy05RE2XNzJvKpprYZFmr\n05C/BfxjrzINVRFWnT8/D/i70lkmMx2yRsR7gTFgdYlcE5koa2a+NzOfRivnW0tlO9T4rBFxLPBe\n+q9InOg9XQUsBc6k9UH30ULRfsIEWWcAJwEvAN4FfLpqaSruCJ8Br6fPvpxNkHU58HvV79XvAVeX\nynaoCbK+EfjdiLgDOAHYWyrbQdUXl/OBZwANWq30vzrBqsVbxCfKGhFvKJtqYm1k/d/AVzLzq73K\nNFRFGK0f4m9n5ubq+eaIWARQ3ffTqYhDs/azn8gaERcCrwQuqJp4+8WR3tf/S/+cioAnZ11K64Pj\nuxFxP63m8m9HxMKC+Q560nuamZszc39mHgD+CijeKXucQ///HwA+V52J+BZwgNaEvv1got+rGcBv\nAJ8qlmpih2a9EPhc9fjv6OOfgczckJkvy8xfoFXcjhZN1/IS4AeZuSUz99F6L38JmFP9DEDrM6Af\nTvMeLms/OmzWiPhDYAT4/V4GGrYi7NBvkDfQ+rCgur++54kOr+++7R7Bk7JGxMuBi4HzMnN3sVQT\nOzTr6eNeOw/Y0PNEh/fjrJl5V2aenJlLMnMJreLhP2XmQyUDVg59TxeNe+3VtE739ItDf68+T6uf\nDRHxTGAWsLVArolM9BnwEmBDZj5QIM+RHJq1CfxK9fhsoJ9OnR7683pydX8U8D5aHd5L2wS8ICKO\nrVpmzwG+B9wKvKZap1/+Zk2UdX3hTIczYdaIeDNwLvD66stj7/RqBEDpG60OeNuA2eOWzQNuofUB\ncQswt3TOI2R9Na0/vE8Am4E1pXMeIet9wA+BO6tbX4w4PEzWz9IqEtYBXwBOKZ3zcFkPef1++mDE\n0WHe078F7qre0xuARaVzHiHrLOC66mfg28DZpXMe6f8f+DjwltL52nhff5lWH6bvArcDv1A65xGy\nvh34l+r2EaqZZErfgA/S+lJ4d/U79RTgZ2h1HL+PVgvjU0rnPELWt1V/s8ZoFeVXlc55hKxjtFpA\nD/7N6tnIY6ctkiRJKmDYTkfNTsRJAAABrUlEQVRKkiT1BYswSZKkAizCJEmSCrAIkyRJKsAiTJIk\nqQCLMEmSpAIswiRpEhFxdOkMkqYfizBJ00pEfCgi3j7u+WUR8baIeFdE/HM1sfgHx73++Yi4IyLu\niYiLxi1/LCL+KCJuB36xx/8MSUPAIkzSdHM11XRk1VQ0r6M1y8TptOYwPBP4hYj4z9X6b8zWvIHL\ngLdFxLxq+XHA3Zn5/Mz8Wi//AZKGw4zJV5GkwZGZ90fEtoh4DrAA+A7wXOBl1WOA42kVZV+hVXi9\nulr+tGr5NmA/rWmtJKkWFmGSpqOrgN8GFgLX0Jqo9/LM/D/jV4qIs2hNiv2Lmbk7Im4Dnlq9/G+Z\nub9XgSUNH09HSpqO/h54Oa0WsDXV7Y0RcTxARJwSEScDs4FHqwLsDOAFpQJLGj62hEmadjJzb0Tc\nCmyvWrNuiohnA/8UEQCPAW8A/hF4S0SsA74PfLNUZknDJzKzdAZJ6qqqQ/63gddm5r2l80jSRDwd\nKWlaiYifBe4DbrEAk9TPbAmTJEkqwJYwSZKkAizCJEmSCrAIkyRJKsAiTJIkqQCLMEmSpAL+P/Za\npFIC4ZCRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0b4b4940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "sns.boxplot(\"year\", \"mpg\", data = df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Association plot between 2 categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a0b70fd68>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/eWykhxCSw3n9LTaWJlddR0qnLuAcm/8znl1zZpJ8bGeaXPF7Elu0Zs3Y+9g1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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0b4b4fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(df.corr(), cmap=sns.color_palette(\"RdBu\", 10), annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a0afd50b8>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iREREJJBUYkRERCSQdGJvFdYwJcbvCCLlIrlmNb8jhLyMHbv8jlAl1I/X3+WD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\nklTEiIiISCSpiBEREZFIUhEjIiIikfT/r7nCLLFHeUgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0b327240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 8))\n",
    "aggr = df.groupby([\"year\", \"cylinders\"])[\"mpg\"].agg(np.mean).unstack()\n",
    "sns.heatmap(aggr, cmap=sns.color_palette(\"Blues\", n_colors= 10), annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classificaition plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
       "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
       "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
       "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
       "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
       "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris = pd.read_csv(\"https://raw.githubusercontent.com/abulbasar/data/master/iris.csv\")\n",
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a11536780>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2Y0n/4e6vhlkMAXkAmZDvQemg7y/ftwsQtUwH5H8i6UUz+6OZrTSzVWa2sm8l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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a11536978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "x1, x2 = \"SepalLengthCm\", \"PetalLengthCm\"\n",
    "cmap = sns.color_palette(\"husl\", n_colors=3)\n",
    "for i, c in enumerate(iris.Species.unique()):\n",
    "    iris[iris.Species == c].plot.scatter(x1, x2, color = cmap[i], label = c, ax = ax)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# QQ Plot for normality test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3l9hqSKUnRFJPJGvCMLN9gAb4OmEAOOc+ildQIiLJLv+C+zynMI1hdOZYZjON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\nZjVwEmZmZmZWg/8A2ziiJp+iuW4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0b8b16d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy.stats as stats\n",
    "p = stats.probplot(df[\"mpg\"], dist=\"norm\", plot=plt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
